Question: I need a solution that enables me to automate data science workflows and deploy models with a standardized process - do you know of any tools that can do this?

Obviously AI is a no-code AI platform that automates data science workflows so you can build and deploy machine learning models without a lot of programming. It's good for a variety of use cases like classification, regression and time series forecasting, and it offers features like rapid model development, one-click deployment, automated model monitoring and real-time REST APIs for integration and visualization.

Dataloop is another powerful option, combining data curation, model management, pipeline orchestration and human feedback to speed up AI application development. It offers automated preprocessing, embeddings for similarity detection, human feedback integration and a marketplace for existing models and pipelines. Dataloop also has high security standards with GDPR and SOC 2 Type II compliance.

For an open-source option, MLflow offers an end-to-end MLOps platform for the full ML project lifecycle. It tracks experiments, logs and manages models across different environments, supporting libraries like PyTorch and TensorFlow. MLflow is free to use, so it's a good option to improve collaboration, transparency and efficiency in ML workflows.